Comparing Dataset A and Dataset B

Dataset statistics

	Dataset A	Dataset B
Number of variables	12	12
Number of observations	446	446
Missing cells	449	444
Missing cells (%)	8.4%	8.3%
Duplicate rows	0	0
Duplicate rows (%)	0.0%	0.0%
Total size in memory	45.3 KiB	45.3 KiB
Average record size in memory	104.0 B	104.0 B

Variable types

	Dataset A	Dataset B
Numeric	5	5
Categorical	4	4
Text	3	3

Alerts

Dataset A	Dataset B
`Survived` is highly overall correlated with `Sex`	`Survived` is highly overall correlated with `Sex`	High Correlation
`Sex` is highly overall correlated with `Survived`	`Sex` is highly overall correlated with `Survived`	High Correlation
`Age` has 99 (22.2%) missing values	`Age` has 93 (20.9%) missing values	Missing
`Cabin` has 350 (78.5%) missing values	`Cabin` has 351 (78.7%) missing values	Missing
`PassengerId` has unique values	`PassengerId` has unique values	Unique
`Name` has unique values	`Name` has unique values	Unique
`SibSp` has 306 (68.6%) zeros	`SibSp` has 312 (70.0%) zeros	Zeros
`Parch` has 346 (77.6%) zeros	`Parch` has 347 (77.8%) zeros	Zeros
`Fare` has 6 (1.3%) zeros	`Fare` has 6 (1.3%) zeros	Zeros
Alert not present in this dataset	`Fare` is highly overall correlated with `Pclass`	High Correlation
Alert not present in this dataset	`Pclass` is highly overall correlated with `Fare`	High Correlation

Reproduction

	Dataset A	Dataset B
Analysis started	2023-06-21 12:53:43.621477	2023-06-21 12:53:48.247868
Analysis finished	2023-06-21 12:53:48.246091	2023-06-21 12:53:52.634410
Duration	4.62 seconds	4.39 seconds
Software version	ydata-profiling v0.0.dev0	ydata-profiling v0.0.dev0
Download configuration	config.json	config.json

PassengerId
Real number (ℝ)

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	435.98655	454.1861

	Dataset A	Dataset B
Minimum	1	2
Maximum	891	890
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	1	2
5-th percentile	46.5	47.25
Q1	204.5	240.25
median	420.5	456.5
Q3	660	669.75
95-th percentile	855	854.75
Maximum	891	890
Range	890	888
Interquartile range (IQR)	455.5	429.5

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	260.29785	258.15885
Coefficient of variation (CV)	0.59703184	0.56839883
Kurtosis	-1.1940922	-1.1837863
Mean	435.98655	454.1861
Median Absolute Deviation (MAD)	225.5	215.5
Skewness	0.087904493	-0.038801394
Sum	194450	202567
Variance	67754.971	66645.99
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
819	1	0.2%
546	1	0.2%
864	1	0.2%
543	1	0.2%
318	1	0.2%
420	1	0.2%
49	1	0.2%
512	1	0.2%
505	1	0.2%
499	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
400	1	0.2%
97	1	0.2%
191	1	0.2%
121	1	0.2%
465	1	0.2%
71	1	0.2%
746	1	0.2%
329	1	0.2%
847	1	0.2%
759	1	0.2%
Other values (436)	436	97.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	1	0.2%
6	1	0.2%
7	1	0.2%
9	1	0.2%
10	1	0.2%
12	1	0.2%
14	1	0.2%
15	1	0.2%
17	1	0.2%
21	1	0.2%

Value	Count	Frequency (%)
2	1	0.2%
3	1	0.2%
4	1	0.2%
5	1	0.2%
7	1	0.2%
8	1	0.2%
10	1	0.2%
14	1	0.2%
15	1	0.2%
16	1	0.2%

Value	Count	Frequency (%)
2	1	0.2%
3	1	0.2%
4	1	0.2%
5	1	0.2%
7	1	0.2%
8	1	0.2%
10	1	0.2%
14	1	0.2%
15	1	0.2%
16	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
6	1	0.2%
7	1	0.2%
9	1	0.2%
10	1	0.2%
12	1	0.2%
14	1	0.2%
15	1	0.2%
17	1	0.2%
21	1	0.2%

Survived
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

0	271
1	175

0	276
1	170

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	2	2
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	0	1
2nd row	1	0
3rd row	0	1
4th row	1	0
5th row	0	0

Common Values

Value	Count	Frequency (%)
0	271	60.8%
1	175	39.2%

Value	Count	Frequency (%)
0	276	61.9%
1	170	38.1%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
0	271	60.8%
1	175	39.2%

Value	Count	Frequency (%)
0	276	61.9%
1	170	38.1%

Most occurring characters

Value	Count	Frequency (%)
0	271	60.8%
1	175	39.2%

Value	Count	Frequency (%)
0	276	61.9%
1	170	38.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	271	60.8%
1	175	39.2%

Value	Count	Frequency (%)
0	276	61.9%
1	170	38.1%

Most occurring scripts

Value	Count	Frequency (%)
Common	446	100.0%

Value	Count	Frequency (%)
Common	446	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	271	60.8%
1	175	39.2%

Value	Count	Frequency (%)
0	276	61.9%
1	170	38.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	446	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	271	60.8%
1	175	39.2%

Value	Count	Frequency (%)
0	276	61.9%
1	170	38.1%

Pclass
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

3	249
1	108
2	89

3	257
1	103
2	86

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	3	2
2nd row	2	3
3rd row	3	3
4th row	3	2
5th row	3	3

Common Values

Value	Count	Frequency (%)
3	249	55.8%
1	108	24.2%
2	89	20.0%

Value	Count	Frequency (%)
3	257	57.6%
1	103	23.1%
2	86	19.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
3	249	55.8%
1	108	24.2%
2	89	20.0%

Value	Count	Frequency (%)
3	257	57.6%
1	103	23.1%
2	86	19.3%

Most occurring characters

Value	Count	Frequency (%)
3	249	55.8%
1	108	24.2%
2	89	20.0%

Value	Count	Frequency (%)
3	257	57.6%
1	103	23.1%
2	86	19.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Value	Count	Frequency (%)
Decimal Number	446	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	249	55.8%
1	108	24.2%
2	89	20.0%

Value	Count	Frequency (%)
3	257	57.6%
1	103	23.1%
2	86	19.3%

Most occurring scripts

Value	Count	Frequency (%)
Common	446	100.0%

Value	Count	Frequency (%)
Common	446	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	249	55.8%
1	108	24.2%
2	89	20.0%

Value	Count	Frequency (%)
3	257	57.6%
1	103	23.1%
2	86	19.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	446	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	249	55.8%
1	108	24.2%
2	89	20.0%

Value	Count	Frequency (%)
3	257	57.6%
1	103	23.1%
2	86	19.3%

Name
['Text', 'Text']

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	82	65
Median length	49	47
Mean length	26.860987	26.697309
Min length	14	12

Characters and Unicode

	Dataset A	Dataset B
Total characters	11980	11907
Distinct characters	60	60
Distinct categories	7	7 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	446	446 ?
Unique (%)	100.0%	100.0%

Sample

	Dataset A	Dataset B
1st row	Holm, Mr. John Fredrik Alexander	Trout, Mrs. William H (Jessie L)
2nd row	Davies, Master. John Morgan Jr	Karlsson, Mr. Nils August
3rd row	Sage, Master. Thomas Henry	de Mulder, Mr. Theodore
4th row	Madsen, Mr. Fridtjof Arne	McKane, Mr. Peter David
5th row	Dahlberg, Miss. Gerda Ulrika	Alhomaki, Mr. Ilmari Rudolf

Value	Count	Frequency (%)
mr	263	14.6%
miss	86	4.8%
mrs	63	3.5%
william	35	1.9%
john	24	1.3%
master	24	1.3%
henry	21	1.2%
james	15	0.8%
charles	12	0.7%
joseph	11	0.6%
Other values (886)	1248	69.3%

Value	Count	Frequency (%)
mr	263	14.7%
miss	89	5.0%
mrs	63	3.5%
william	31	1.7%
john	26	1.5%
master	18	1.0%
george	17	0.9%
henry	16	0.9%
charles	14	0.8%
thomas	13	0.7%
Other values (894)	1241	69.3%

Most occurring characters

Value	Count	Frequency (%)
	1356	11.3%
r	998	8.3%
e	883	7.4%
a	821	6.9%
i	642	5.4%
s	640	5.3%
n	635	5.3%
M	571	4.8%
l	522	4.4%
o	507	4.2%
Other values (50)	4405	36.8%

Value	Count	Frequency (%)
	1347	11.3%
r	956	8.0%
e	854	7.2%
a	816	6.9%
n	657	5.5%
i	654	5.5%
s	644	5.4%
M	563	4.7%
l	541	4.5%
o	510	4.3%
Other values (50)	4365	36.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	7706	64.3%
Uppercase Letter	1816	15.2%
Space Separator	1356	11.3%
Other Punctuation	951	7.9%
Close Punctuation	71	0.6%
Open Punctuation	71	0.6%
Dash Punctuation	9	0.1%

Value	Count	Frequency (%)
Lowercase Letter	7665	64.4%
Uppercase Letter	1807	15.2%
Space Separator	1347	11.3%
Other Punctuation	944	7.9%
Close Punctuation	68	0.6%
Open Punctuation	68	0.6%
Dash Punctuation	8	0.1%

Most frequent character per category

Space Separator

Value	Count	Frequency (%)
	1356	100.0%

Value	Count	Frequency (%)
	1347	100.0%

Lowercase Letter

Value	Count	Frequency (%)
r	998	13.0%
e	883	11.5%
a	821	10.7%
i	642	8.3%
s	640	8.3%
n	635	8.2%
l	522	6.8%
o	507	6.6%
t	346	4.5%
h	272	3.5%
Other values (16)	1440	18.7%

Value	Count	Frequency (%)
r	956	12.5%
e	854	11.1%
a	816	10.6%
n	657	8.6%
i	654	8.5%
s	644	8.4%
l	541	7.1%
o	510	6.7%
t	334	4.4%
h	254	3.3%
Other values (16)	1445	18.9%

Uppercase Letter

Value	Count	Frequency (%)
M	571	31.4%
J	119	6.6%
A	115	6.3%
H	107	5.9%
C	96	5.3%
S	93	5.1%
E	84	4.6%
W	76	4.2%
B	68	3.7%
L	55	3.0%
Other values (15)	432	23.8%

Value	Count	Frequency (%)
M	563	31.2%
A	111	6.1%
J	107	5.9%
H	97	5.4%
C	87	4.8%
S	87	4.8%
E	83	4.6%
B	70	3.9%
L	70	3.9%
W	66	3.7%
Other values (15)	466	25.8%

Other Punctuation

Value	Count	Frequency (%)
.	447	47.0%
,	446	46.9%
"	52	5.5%
'	5	0.5%
/	1	0.1%

Value	Count	Frequency (%)
.	446	47.2%
,	446	47.2%
"	48	5.1%
'	3	0.3%
/	1	0.1%

Close Punctuation

Value	Count	Frequency (%)
)	71	100.0%

Value	Count	Frequency (%)
)	68	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	71	100.0%

Value	Count	Frequency (%)
(	68	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	9	100.0%

Value	Count	Frequency (%)
-	8	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	9522	79.5%
Common	2458	20.5%

Value	Count	Frequency (%)
Latin	9472	79.5%
Common	2435	20.5%

Most frequent character per script

Common

Value	Count	Frequency (%)
	1356	55.2%
.	447	18.2%
,	446	18.1%
)	71	2.9%
(	71	2.9%
"	52	2.1%
-	9	0.4%
'	5	0.2%
/	1	< 0.1%

Value	Count	Frequency (%)
	1347	55.3%
.	446	18.3%
,	446	18.3%
)	68	2.8%
(	68	2.8%
"	48	2.0%
-	8	0.3%
'	3	0.1%
/	1	< 0.1%

Latin

Value	Count	Frequency (%)
r	998	10.5%
e	883	9.3%
a	821	8.6%
i	642	6.7%
s	640	6.7%
n	635	6.7%
M	571	6.0%
l	522	5.5%
o	507	5.3%
t	346	3.6%
Other values (41)	2957	31.1%

Value	Count	Frequency (%)
r	956	10.1%
e	854	9.0%
a	816	8.6%
n	657	6.9%
i	654	6.9%
s	644	6.8%
M	563	5.9%
l	541	5.7%
o	510	5.4%
t	334	3.5%
Other values (41)	2943	31.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	11980	100.0%

Value	Count	Frequency (%)
ASCII	11907	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	1356	11.3%
r	998	8.3%
e	883	7.4%
a	821	6.9%
i	642	5.4%
s	640	5.3%
n	635	5.3%
M	571	4.8%
l	522	4.4%
o	507	4.2%
Other values (50)	4405	36.8%

Value	Count	Frequency (%)
	1347	11.3%
r	956	8.0%
e	854	7.2%
a	816	6.9%
n	657	5.5%
i	654	5.5%
s	644	5.4%
M	563	4.7%
l	541	4.5%
o	510	4.3%
Other values (50)	4365	36.7%

Sex
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

male	295
female	151

male	292
female	154

Length

	Dataset A	Dataset B
Max length	6	6
Median length	4	4
Mean length	4.67713	4.690583
Min length	4	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	2086	2092
Distinct characters	5	5
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	male	female
2nd row	male	male
3rd row	male	male
4th row	male	male
5th row	female	male

Common Values

Value	Count	Frequency (%)
male	295	66.1%
female	151	33.9%

Value	Count	Frequency (%)
male	292	65.5%
female	154	34.5%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
male	295	66.1%
female	151	33.9%

Value	Count	Frequency (%)
male	292	65.5%
female	154	34.5%

Most occurring characters

Value	Count	Frequency (%)
e	597	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	151	7.2%

Value	Count	Frequency (%)
e	600	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	154	7.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	2086	100.0%

Value	Count	Frequency (%)
Lowercase Letter	2092	100.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	597	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	151	7.2%

Value	Count	Frequency (%)
e	600	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	154	7.4%

Most occurring scripts

Value	Count	Frequency (%)
Latin	2086	100.0%

Value	Count	Frequency (%)
Latin	2092	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	597	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	151	7.2%

Value	Count	Frequency (%)
e	600	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	154	7.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2086	100.0%

Value	Count	Frequency (%)
ASCII	2092	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	597	28.6%
m	446	21.4%
a	446	21.4%
l	446	21.4%
f	151	7.2%

Value	Count	Frequency (%)
e	600	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	154	7.4%

Age
Real number (ℝ)

	Dataset A	Dataset B
Distinct	69	80
Distinct (%)	19.9%	22.7%
Missing	99	93
Missing (%)	22.2%	20.9%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	29.211816	29.832861

	Dataset A	Dataset B
Minimum	0.42	0.42
Maximum	64	80
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0.42	0.42
5-th percentile	4	3.6
Q1	20	20
median	28	28.5
Q3	38.5	39
95-th percentile	53.7	58
Maximum	64	80
Range	63.58	79.58
Interquartile range (IQR)	18.5	19

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	13.721645	14.792112
Coefficient of variation (CV)	0.46972929	0.49583283
Kurtosis	-0.24656624	0.20666063
Mean	29.211816	29.832861
Median Absolute Deviation (MAD)	9	9.5
Skewness	0.12739559	0.36664262
Sum	10136.5	10531
Variance	188.28355	218.80658
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
25	14	3.1%
21	13	2.9%
19	13	2.9%
30	13	2.9%
22	12	2.7%
27	12	2.7%
24	11	2.5%
20	11	2.5%
29	10	2.2%
36	10	2.2%
Other values (59)	228	51.1%
(Missing)	99	22.2%

Value	Count	Frequency (%)
30	16	3.6%
19	15	3.4%
28	14	3.1%
22	13	2.9%
18	13	2.9%
25	13	2.9%
32	13	2.9%
24	13	2.9%
21	11	2.5%
31	11	2.5%
Other values (70)	221	49.6%
(Missing)	93	20.9%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	1	0.2%
0.83	1	0.2%
1	4	0.9%
2	6	1.3%
3	2	0.4%
4	5	1.1%
5	2	0.4%
7	3	0.7%
8	2	0.4%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	2	0.4%
0.83	2	0.4%
0.92	1	0.2%
1	3	0.7%
2	6	1.3%
3	3	0.7%
4	5	1.1%
5	1	0.2%
7	1	0.2%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	2	0.4%
0.83	2	0.4%
0.92	1	0.2%
1	3	0.7%
2	6	1.3%
3	3	0.7%
4	5	1.1%
5	1	0.2%
7	1	0.2%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	1	0.2%
0.83	1	0.2%
1	4	0.9%
2	6	1.3%
3	2	0.4%
4	5	1.1%
5	2	0.4%
7	3	0.7%
8	2	0.4%

SibSp
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.54484305	0.48206278

	Dataset A	Dataset B
Minimum	0	0
Maximum	8	8
Zeros	306	312
Zeros (%)	68.6%	70.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	1	1
95-th percentile	2	2
Maximum	8	8
Range	8	8
Interquartile range (IQR)	1	1

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	1.2439545	1.04651
Coefficient of variation (CV)	2.2831428	2.1708998
Kurtosis	19.53937	19.864923
Mean	0.54484305	0.48206278
Median Absolute Deviation (MAD)	0	0
Skewness	4.0519772	3.8528422
Sum	243	215
Variance	1.5474228	1.0951832
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	306	68.6%
1	105	23.5%
2	13	2.9%
8	7	1.6%
4	7	1.6%
3	6	1.3%
5	2	0.4%

Value	Count	Frequency (%)
0	312	70.0%
1	101	22.6%
2	12	2.7%
3	8	1.8%
4	8	1.8%
8	3	0.7%
5	2	0.4%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	306	68.6%
1	105	23.5%
2	13	2.9%
3	6	1.3%
4	7	1.6%
5	2	0.4%
8	7	1.6%

Value	Count	Frequency (%)
0	312	70.0%
1	101	22.6%
2	12	2.7%
3	8	1.8%
4	8	1.8%
5	2	0.4%
8	3	0.7%

Value	Count	Frequency (%)
0	312	70.0%
1	101	22.6%
2	12	2.7%
3	8	1.8%
4	8	1.8%
5	2	0.4%
8	3	0.7%

Value	Count	Frequency (%)
0	306	68.6%
1	105	23.5%
2	13	2.9%
3	6	1.3%
4	7	1.6%
5	2	0.4%
8	7	1.6%

Parch
Real number (ℝ)

	Dataset A	Dataset B
Distinct	6	6
Distinct (%)	1.3%	1.3%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.367713	0.36547085

	Dataset A	Dataset B
Minimum	0	0
Maximum	5	5
Zeros	346	347
Zeros (%)	77.6%	77.8%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	0	0
95-th percentile	2	2
Maximum	5	5
Range	5	5
Interquartile range (IQR)	0	0

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	0.81515845	0.81754394
Coefficient of variation (CV)	2.2168333	2.2369607
Kurtosis	10.240577	10.291075
Mean	0.367713	0.36547085
Median Absolute Deviation (MAD)	0	0
Skewness	2.8823125	2.9046286
Sum	164	163
Variance	0.6644833	0.66837809
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
0	346	77.6%
1	54	12.1%
2	38	8.5%
5	4	0.9%
4	2	0.4%
3	2	0.4%

Value	Count	Frequency (%)
0	347	77.8%
1	54	12.1%
2	36	8.1%
5	4	0.9%
3	3	0.7%
4	2	0.4%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	346	77.6%
1	54	12.1%
2	38	8.5%
3	2	0.4%
4	2	0.4%
5	4	0.9%

Value	Count	Frequency (%)
0	347	77.8%
1	54	12.1%
2	36	8.1%
3	3	0.7%
4	2	0.4%
5	4	0.9%

Value	Count	Frequency (%)
0	347	77.8%
1	54	12.1%
2	36	8.1%
3	3	0.7%
4	2	0.4%
5	4	0.9%

Value	Count	Frequency (%)
0	346	77.6%
1	54	12.1%
2	38	8.5%
3	2	0.4%
4	2	0.4%
5	4	0.9%

Ticket
['Text', 'Text']

	Dataset A	Dataset B
Distinct	389	388
Distinct (%)	87.2%	87.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	18	18
Median length	17	17
Mean length	6.6995516	6.7623318
Min length	3	3

Characters and Unicode

	Dataset A	Dataset B
Total characters	2988	3016
Distinct characters	35	32
Distinct categories	5	5 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	344	344 ?
Unique (%)	77.1%	77.1%

Sample

	Dataset A	Dataset B
1st row	C 7075	240929
2nd row	C.A. 33112	350060
3rd row	CA. 2343	345774
4th row	C 17369	28403
5th row	7552	SOTON/O2 3101287

Value	Count	Frequency (%)
pc	29	5.2%
c.a	12	2.1%
ca	9	1.6%
a/5	7	1.2%
2343	7	1.2%
2	7	1.2%
ston/o	7	1.2%
382652	5	0.9%
soton/o.q	5	0.9%
w./c	4	0.7%
Other values (410)	470	83.6%

Value	Count	Frequency (%)
pc	26	4.7%
c.a	9	1.6%
a/5	7	1.3%
2	7	1.3%
ston/o	7	1.3%
ca	6	1.1%
347082	6	1.1%
soton/o.q	5	0.9%
soton/oq	5	0.9%
ston/o2	5	0.9%
Other values (407)	476	85.2%

Most occurring characters

Value	Count	Frequency (%)
3	390	13.1%
1	337	11.3%
2	293	9.8%
7	257	8.6%
6	238	8.0%
4	210	7.0%
0	195	6.5%
5	194	6.5%
9	159	5.3%
8	129	4.3%
Other values (25)	586	19.6%

Value	Count	Frequency (%)
3	390	12.9%
1	336	11.1%
2	296	9.8%
7	233	7.7%
4	230	7.6%
6	222	7.4%
0	208	6.9%
5	193	6.4%
9	165	5.5%
8	145	4.8%
Other values (22)	598	19.8%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	2402	80.4%
Uppercase Letter	316	10.6%
Other Punctuation	142	4.8%
Space Separator	116	3.9%
Lowercase Letter	12	0.4%

Value	Count	Frequency (%)
Decimal Number	2418	80.2%
Uppercase Letter	323	10.7%
Other Punctuation	153	5.1%
Space Separator	113	3.7%
Lowercase Letter	9	0.3%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	390	16.2%
1	337	14.0%
2	293	12.2%
7	257	10.7%
6	238	9.9%
4	210	8.7%
0	195	8.1%
5	194	8.1%
9	159	6.6%
8	129	5.4%

Value	Count	Frequency (%)
3	390	16.1%
1	336	13.9%
2	296	12.2%
7	233	9.6%
4	230	9.5%
6	222	9.2%
0	208	8.6%
5	193	8.0%
9	165	6.8%
8	145	6.0%

Space Separator

Value	Count	Frequency (%)
	116	100.0%

Value	Count	Frequency (%)
	113	100.0%

Other Punctuation

Value	Count	Frequency (%)
.	94	66.2%
/	48	33.8%

Value	Count	Frequency (%)
.	102	66.7%
/	51	33.3%

Uppercase Letter

Value	Count	Frequency (%)
C	75	23.7%
O	48	15.2%
P	42	13.3%
A	40	12.7%
S	37	11.7%
N	19	6.0%
T	17	5.4%
W	9	2.8%
Q	8	2.5%
I	5	1.6%
Other values (6)	16	5.1%

Value	Count	Frequency (%)
O	65	20.1%
C	63	19.5%
P	42	13.0%
S	40	12.4%
A	33	10.2%
N	25	7.7%
T	23	7.1%
Q	10	3.1%
W	7	2.2%
F	5	1.5%
Other values (5)	10	3.1%

Lowercase Letter

Value	Count	Frequency (%)
s	3	25.0%
a	3	25.0%
r	2	16.7%
i	2	16.7%
l	1	8.3%
e	1	8.3%

Value	Count	Frequency (%)
a	3	33.3%
r	2	22.2%
i	2	22.2%
s	2	22.2%

Most occurring scripts

Value	Count	Frequency (%)
Common	2660	89.0%
Latin	328	11.0%

Value	Count	Frequency (%)
Common	2684	89.0%
Latin	332	11.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	390	14.7%
1	337	12.7%
2	293	11.0%
7	257	9.7%
6	238	8.9%
4	210	7.9%
0	195	7.3%
5	194	7.3%
9	159	6.0%
8	129	4.8%
Other values (3)	258	9.7%

Value	Count	Frequency (%)
3	390	14.5%
1	336	12.5%
2	296	11.0%
7	233	8.7%
4	230	8.6%
6	222	8.3%
0	208	7.7%
5	193	7.2%
9	165	6.1%
8	145	5.4%
Other values (3)	266	9.9%

Latin

Value	Count	Frequency (%)
C	75	22.9%
O	48	14.6%
P	42	12.8%
A	40	12.2%
S	37	11.3%
N	19	5.8%
T	17	5.2%
W	9	2.7%
Q	8	2.4%
I	5	1.5%
Other values (12)	28	8.5%

Value	Count	Frequency (%)
O	65	19.6%
C	63	19.0%
P	42	12.7%
S	40	12.0%
A	33	9.9%
N	25	7.5%
T	23	6.9%
Q	10	3.0%
W	7	2.1%
F	5	1.5%
Other values (9)	19	5.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	2988	100.0%

Value	Count	Frequency (%)
ASCII	3016	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	390	13.1%
1	337	11.3%
2	293	9.8%
7	257	8.6%
6	238	8.0%
4	210	7.0%
0	195	6.5%
5	194	6.5%
9	159	5.3%
8	129	4.3%
Other values (25)	586	19.6%

Value	Count	Frequency (%)
3	390	12.9%
1	336	11.1%
2	296	9.8%
7	233	7.7%
4	230	7.6%
6	222	7.4%
0	208	6.9%
5	193	6.4%
9	165	5.5%
8	145	4.8%
Other values (22)	598	19.8%

Fare
Real number (ℝ)

	Dataset A	Dataset B
Distinct	188	181
Distinct (%)	42.2%	40.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	31.985706	28.221

	Dataset A	Dataset B
Minimum	0	0
Maximum	512.3292	263
Zeros	6	6
Zeros (%)	1.3%	1.3%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	7.162525	7.225
Q1	7.9031	7.8958
median	13.89585	13.68125
Q3	30.0708	30.0708
95-th percentile	120	90
Maximum	512.3292	263
Range	512.3292	263
Interquartile range (IQR)	22.1677	22.175

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	50.411634	34.576378
Coefficient of variation (CV)	1.5760676	1.2252003
Kurtosis	39.132238	13.121447
Mean	31.985706	28.221
Median Absolute Deviation (MAD)	6.64585	6.13125
Skewness	5.1942457	3.0872007
Sum	14265.625	12586.566
Variance	2541.3328	1195.5259
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
13	21	4.7%
7.8958	21	4.7%
8.05	20	4.5%
26	17	3.8%
7.75	17	3.8%
10.5	11	2.5%
7.925	9	2.0%
26.55	7	1.6%
7.2292	7	1.6%
8.6625	7	1.6%
Other values (178)	309	69.3%

Value	Count	Frequency (%)
8.05	28	6.3%
13	24	5.4%
7.75	23	5.2%
7.8958	22	4.9%
26	15	3.4%
7.925	10	2.2%
7.775	8	1.8%
7.2292	7	1.6%
10.5	7	1.6%
7.8542	7	1.6%
Other values (171)	295	66.1%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	6	1.3%
4.0125	1	0.2%
5	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.75	1	0.2%
6.8583	1	0.2%
6.95	1	0.2%
6.975	1	0.2%

Value	Count	Frequency (%)
0	6	1.3%
6.2375	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.75	1	0.2%
6.8583	1	0.2%
6.95	1	0.2%
6.975	2	0.4%
7.05	4	0.9%
7.125	3	0.7%

Value	Count	Frequency (%)
0	6	1.3%
6.2375	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.75	1	0.2%
6.8583	1	0.2%
6.95	1	0.2%
6.975	2	0.4%
7.05	4	0.9%
7.125	3	0.7%

Value	Count	Frequency (%)
0	6	1.3%
4.0125	1	0.2%
5	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.75	1	0.2%
6.8583	1	0.2%
6.95	1	0.2%
6.975	1	0.2%

Cabin
['Text', 'Text']

	Dataset A	Dataset B
Distinct	85	83
Distinct (%)	88.5%	87.4%
Missing	350	351
Missing (%)	78.5%	78.7%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	11	11
Median length	3	3
Mean length	3.4791667	3.3789474
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	334	321
Distinct characters	18	18
Distinct categories	3	3 ?
Distinct scripts	2	2 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	75	71 ?
Unique (%)	78.1%	74.7%

Sample

	Dataset A	Dataset B
1st row	D49	A10
2nd row	E8	E33
3rd row	B77	D17
4th row	B5	E49
5th row	C103	C93

Value	Count	Frequency (%)
b96	3	2.8%
b98	3	2.8%
e101	2	1.8%
e8	2	1.8%
d	2	1.8%
c124	2	1.8%
e44	2	1.8%
g6	2	1.8%
c26	2	1.8%
c22	2	1.8%
Other values (85)	87	79.8%

Value	Count	Frequency (%)
c22	2	1.9%
b58	2	1.9%
c26	2	1.9%
c93	2	1.9%
b20	2	1.9%
e33	2	1.9%
e101	2	1.9%
b60	2	1.9%
e44	2	1.9%
c126	2	1.9%
Other values (79)	84	80.8%

Most occurring characters

Value	Count	Frequency (%)
1	33	9.9%
B	32	9.6%
C	31	9.3%
3	29	8.7%
2	25	7.5%
4	24	7.2%
D	21	6.3%
6	20	6.0%
9	19	5.7%
0	17	5.1%
Other values (8)	83	24.9%

Value	Count	Frequency (%)
C	32	10.0%
2	32	10.0%
1	28	8.7%
3	24	7.5%
B	23	7.2%
6	22	6.9%
4	22	6.9%
8	20	6.2%
E	19	5.9%
5	18	5.6%
Other values (8)	81	25.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	212	63.5%
Uppercase Letter	109	32.6%
Space Separator	13	3.9%

Value	Count	Frequency (%)
Decimal Number	208	64.8%
Uppercase Letter	104	32.4%
Space Separator	9	2.8%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	33	15.6%
3	29	13.7%
2	25	11.8%
4	24	11.3%
6	20	9.4%
9	19	9.0%
0	17	8.0%
8	16	7.5%
5	15	7.1%
7	14	6.6%

Value	Count	Frequency (%)
2	32	15.4%
1	28	13.5%
3	24	11.5%
6	22	10.6%
4	22	10.6%
8	20	9.6%
5	18	8.7%
0	17	8.2%
9	13	6.2%
7	12	5.8%

Uppercase Letter

Value	Count	Frequency (%)
B	32	29.4%
C	31	28.4%
D	21	19.3%
E	12	11.0%
A	6	5.5%
F	4	3.7%
G	3	2.8%

Value	Count	Frequency (%)
C	32	30.8%
B	23	22.1%
E	19	18.3%
D	17	16.3%
A	8	7.7%
F	4	3.8%
G	1	1.0%

Space Separator

Value	Count	Frequency (%)
	13	100.0%

Value	Count	Frequency (%)
	9	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	225	67.4%
Latin	109	32.6%

Value	Count	Frequency (%)
Common	217	67.6%
Latin	104	32.4%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	33	14.7%
3	29	12.9%
2	25	11.1%
4	24	10.7%
6	20	8.9%
9	19	8.4%
0	17	7.6%
8	16	7.1%
5	15	6.7%
7	14	6.2%

Value	Count	Frequency (%)
2	32	14.7%
1	28	12.9%
3	24	11.1%
6	22	10.1%
4	22	10.1%
8	20	9.2%
5	18	8.3%
0	17	7.8%
9	13	6.0%
7	12	5.5%

Latin

Value	Count	Frequency (%)
B	32	29.4%
C	31	28.4%
D	21	19.3%
E	12	11.0%
A	6	5.5%
F	4	3.7%
G	3	2.8%

Value	Count	Frequency (%)
C	32	30.8%
B	23	22.1%
E	19	18.3%
D	17	16.3%
A	8	7.7%
F	4	3.8%
G	1	1.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	334	100.0%

Value	Count	Frequency (%)
ASCII	321	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	33	9.9%
B	32	9.6%
C	31	9.3%
3	29	8.7%
2	25	7.5%
4	24	7.2%
D	21	6.3%
6	20	6.0%
9	19	5.7%
0	17	5.1%
Other values (8)	83	24.9%

Value	Count	Frequency (%)
C	32	10.0%
2	32	10.0%
1	28	8.7%
3	24	7.5%
B	23	7.2%
6	22	6.9%
4	22	6.9%
8	20	6.2%
E	19	5.9%
5	18	5.6%
Other values (8)	81	25.2%

Embarked
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

S	317
C	87
Q	42

S	324
C	78
Q	44

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	S	S
2nd row	S	S
3rd row	S	S
4th row	S	S
5th row	S	S

Common Values

Value	Count	Frequency (%)
S	317	71.1%
C	87	19.5%
Q	42	9.4%

Value	Count	Frequency (%)
S	324	72.6%
C	78	17.5%
Q	44	9.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
s	317	71.1%
c	87	19.5%
q	42	9.4%

Value	Count	Frequency (%)
s	324	72.6%
c	78	17.5%
q	44	9.9%

Most occurring characters

Value	Count	Frequency (%)
S	317	71.1%
C	87	19.5%
Q	42	9.4%

Value	Count	Frequency (%)
S	324	72.6%
C	78	17.5%
Q	44	9.9%

Most occurring categories

Value	Count	Frequency (%)
Uppercase Letter	446	100.0%

Value	Count	Frequency (%)
Uppercase Letter	446	100.0%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
S	317	71.1%
C	87	19.5%
Q	42	9.4%

Value	Count	Frequency (%)
S	324	72.6%
C	78	17.5%
Q	44	9.9%

Most occurring scripts

Value	Count	Frequency (%)
Latin	446	100.0%

Value	Count	Frequency (%)
Latin	446	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
S	317	71.1%
C	87	19.5%
Q	42	9.4%

Value	Count	Frequency (%)
S	324	72.6%
C	78	17.5%
Q	44	9.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	446	100.0%

Value	Count	Frequency (%)
ASCII	446	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
S	317	71.1%
C	87	19.5%
Q	42	9.4%

Value	Count	Frequency (%)
S	324	72.6%
C	78	17.5%
Q	44	9.9%

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Auto

Heatmap
Table

Dataset A

Dataset B

Dataset A

	PassengerId	Age	SibSp	Parch	Fare	Survived	Pclass	Sex	Embarked
PassengerId	1.000	0.016	-0.040	0.005	0.012	0.000	0.000	0.128	0.000
Age	0.016	1.000	-0.142	-0.219	0.218	0.084	0.328	0.000	0.000
SibSp	-0.040	-0.142	1.000	0.377	0.461	0.207	0.154	0.172	0.152
Parch	0.005	-0.219	0.377	1.000	0.401	0.138	0.059	0.237	0.058
Fare	0.012	0.218	0.461	0.401	1.000	0.267	0.480	0.211	0.171
Survived	0.000	0.084	0.207	0.138	0.267	1.000	0.319	0.525	0.192
Pclass	0.000	0.328	0.154	0.059	0.480	0.319	1.000	0.117	0.250
Sex	0.128	0.000	0.172	0.237	0.211	0.525	0.117	1.000	0.115
Embarked	0.000	0.000	0.152	0.058	0.171	0.192	0.250	0.115	1.000

Dataset B

	PassengerId	Age	SibSp	Parch	Fare	Survived	Pclass	Sex	Embarked
PassengerId	1.000	0.013	-0.038	-0.014	-0.008	0.105	0.000	0.000	0.000
Age	0.013	1.000	-0.137	-0.241	0.144	0.163	0.245	0.124	0.114
SibSp	-0.038	-0.137	1.000	0.416	0.489	0.184	0.159	0.218	0.076
Parch	-0.014	-0.241	0.416	1.000	0.404	0.179	0.052	0.228	0.081
Fare	-0.008	0.144	0.489	0.404	1.000	0.235	0.548	0.124	0.240
Survived	0.105	0.163	0.184	0.179	0.235	1.000	0.338	0.501	0.120
Pclass	0.000	0.245	0.159	0.052	0.548	0.338	1.000	0.114	0.255
Sex	0.000	0.124	0.218	0.228	0.124	0.501	0.114	1.000	0.096
Embarked	0.000	0.114	0.076	0.081	0.240	0.120	0.255	0.096	1.000

Dataset A

A simple visualization of nullity by column.

Dataset B

A simple visualization of nullity by column.

Dataset A

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset B

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset A

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Dataset B

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

First rows
Last rows

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
818	819	0	3	Holm, Mr. John Fredrik Alexander	male	43.0	0	0	C 7075	6.4500	NaN	S
549	550	1	2	Davies, Master. John Morgan Jr	male	8.0	1	1	C.A. 33112	36.7500	NaN	S
159	160	0	3	Sage, Master. Thomas Henry	male	NaN	8	2	CA. 2343	69.5500	NaN	S
127	128	1	3	Madsen, Mr. Fridtjof Arne	male	24.0	0	0	C 17369	7.1417	NaN	S
882	883	0	3	Dahlberg, Miss. Gerda Ulrika	female	22.0	0	0	7552	10.5167	NaN	S
722	723	0	2	Gillespie, Mr. William Henry	male	34.0	0	0	12233	13.0000	NaN	S
797	798	1	3	Osman, Mrs. Mara	female	31.0	0	0	349244	8.6833	NaN	S
14	15	0	3	Vestrom, Miss. Hulda Amanda Adolfina	female	14.0	0	0	350406	7.8542	NaN	S
603	604	0	3	Torber, Mr. Ernst William	male	44.0	0	0	364511	8.0500	NaN	S
517	518	0	3	Ryan, Mr. Patrick	male	NaN	0	0	371110	24.1500	NaN	Q

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
399	400	1	2	Trout, Mrs. William H (Jessie L)	female	28.0	0	0	240929	12.6500	NaN	S
478	479	0	3	Karlsson, Mr. Nils August	male	22.0	0	0	350060	7.5208	NaN	S
286	287	1	3	de Mulder, Mr. Theodore	male	30.0	0	0	345774	9.5000	NaN	S
397	398	0	2	McKane, Mr. Peter David	male	46.0	0	0	28403	26.0000	NaN	S
840	841	0	3	Alhomaki, Mr. Ilmari Rudolf	male	20.0	0	0	SOTON/O2 3101287	7.9250	NaN	S
406	407	0	3	Widegren, Mr. Carl/Charles Peter	male	51.0	0	0	347064	7.7500	NaN	S
583	584	0	1	Ross, Mr. John Hugo	male	36.0	0	0	13049	40.1250	A10	C
356	357	1	1	Bowerman, Miss. Elsie Edith	female	22.0	0	1	113505	55.0000	E33	S
862	863	1	1	Swift, Mrs. Frederick Joel (Margaret Welles Barron)	female	48.0	0	0	17466	25.9292	D17	S
324	325	0	3	Sage, Mr. George John Jr	male	NaN	8	2	CA. 2343	69.5500	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
382	383	0	3	Tikkanen, Mr. Juho	male	32.0	0	0	STON/O 2. 3101293	7.9250	NaN	S
31	32	1	1	Spencer, Mrs. William Augustus (Marie Eugenie)	female	NaN	1	0	PC 17569	146.5208	B78	C
453	454	1	1	Goldenberg, Mr. Samuel L	male	49.0	1	0	17453	89.1042	C92	C
467	468	0	1	Smart, Mr. John Montgomery	male	56.0	0	0	113792	26.5500	NaN	S
251	252	0	3	Strom, Mrs. Wilhelm (Elna Matilda Persson)	female	29.0	1	1	347054	10.4625	G6	S
195	196	1	1	Lurette, Miss. Elise	female	58.0	0	0	PC 17569	146.5208	B80	C
150	151	0	2	Bateman, Rev. Robert James	male	51.0	0	0	S.O.P. 1166	12.5250	NaN	S
276	277	0	3	Lindblom, Miss. Augusta Charlotta	female	45.0	0	0	347073	7.7500	NaN	S
375	376	1	1	Meyer, Mrs. Edgar Joseph (Leila Saks)	female	NaN	1	0	PC 17604	82.1708	NaN	C
479	480	1	3	Hirvonen, Miss. Hildur E	female	2.0	0	1	3101298	12.2875	NaN	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
857	858	1	1	Daly, Mr. Peter Denis	male	51.0	0	0	113055	26.5500	E17	S
30	31	0	1	Uruchurtu, Don. Manuel E	male	40.0	0	0	PC 17601	27.7208	NaN	C
40	41	0	3	Ahlin, Mrs. Johan (Johanna Persdotter Larsson)	female	40.0	1	0	7546	9.4750	NaN	S
370	371	1	1	Harder, Mr. George Achilles	male	25.0	1	0	11765	55.4417	E50	C
660	661	1	1	Frauenthal, Dr. Henry William	male	50.0	2	0	PC 17611	133.6500	NaN	S
681	682	1	1	Hassab, Mr. Hammad	male	27.0	0	0	PC 17572	76.7292	D49	C
237	238	1	2	Collyer, Miss. Marjorie "Lottie"	female	8.0	0	2	C.A. 31921	26.2500	NaN	S
418	419	0	2	Matthews, Mr. William John	male	30.0	0	0	28228	13.0000	NaN	S
390	391	1	1	Carter, Mr. William Ernest	male	36.0	1	2	113760	120.0000	B96 B98	S
639	640	0	3	Thorneycroft, Mr. Percival	male	NaN	1	0	376564	16.1000	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Overview

Variables

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters

Most occurring categories

Most frequent character per category

Space Separator

Lowercase Letter

Uppercase Letter

Other Punctuation

Close Punctuation

Open Punctuation

Dash Punctuation

Most occurring scripts

Most frequent character per script

Common

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Space Separator

Other Punctuation

Uppercase Letter

Lowercase Letter

Most occurring scripts

Most frequent character per script

Common

Latin

Most occurring blocks

Most frequent character per block

ASCII

Most occurring characters